This workflow fits a model across all of CONUS that predicts whether a location does or does not have trees.
The data consists of vegetation % cover by functional group from across CONUS (from AIM, FIA, LANDFIRE, and RAP), as well as climate variables from DayMet, which have been aggregated into mean interannual conditions accross multiple temporal windows.
Set user defined parameters
print(params)
## $run
## [1] FALSE
##
## $save_figs
## [1] TRUE
##
## $ecoregion
## [1] "CONUS"
##
## $response
## [1] "TotalTreeCover"
##
## $treeThreshold
## [1] 20
##
## $removeTexasLouisianaPlain
## [1] FALSE
##
## $whichSecondBestMod
## [1] "auto"
# set to true if want to run for a limited number of rows (i.e. for code testing)
test_run <- params$test_run
save_figs <- params$save_figs
response <- params$response
fit_sample <- TRUE # fit model to a sample of the data
n_train <- 5e4 # sample size of the training data
n_test <- 1e6 # sample size of the testing data (if this is too big the decile dotplot code throws memory errors)
removeTLP <- params$removeTexasLouisianaPlain
run <- params$run
whichSecondBestMod <- params$whichSecondBestMod
treeThreshold <- params$treeThreshold
Load packages and source functions
# set option so resampled dataset created here reproduces earlier runs of this code with dplyr 1.0.10
source("../../../Functions/glmTransformsIterates.R")
source("../../../Functions/transformPreds.R")
source("../../../Functions/betaLASSO.R")
#source("../../../Functions/StepBeta_mine.R")
#source("src/fig_params.R")
#source("src/modeling_functions.R")
library(betareg)
library(ggspatial)
library(terra)
library(tidyterra)
library(sf)
library(caret)
library(tidyverse)
library(GGally) # for ggpairs()
library(pdp) # for partial dependence plots
library(gridExtra)
library(knitr)
library(patchwork) # for figure insets etc.
library(ggtext)
library(StepBeta)
theme_set(theme_classic())
library(here)
library(rsample)
library(kableExtra)
library(glmnet)
library(USA.state.boundaries)
library(cvms)
library(rsvg)
library(ggimage)
Data compiled in the prepDataForModels.R script
here::i_am("Analysis/VegComposition/ModelFitting/02_ModelFitting_globalTreeModel.Rmd")
modDat <- readRDS( here("Data_processed", "CoverData", "DataForModels_spatiallyAveraged_withSoils_noSf_sampledLANDFIRE.rds")) %>% st_drop_geometry()
We will fit a binomial model that predicts whether or not there are trees at a location. Because the tree cover data we have is continuous between 0 and 100, we convert it to be binomial be forcing any values ≤ % to be 0, and any values > % to be 1.
modDat <- modDat %>%
mutate(TotalTreeCover_binom = replace(TotalTreeCover, TotalTreeCover <=treeThreshold, 0)) %>%
mutate(TotalTreeCover_binom = replace(TotalTreeCover_binom, TotalTreeCover_binom > treeThreshold, 1))
set.seed(1234)
# now, rename columns for brevity
modDat_1 <- modDat %>%
dplyr::select(-c(prcp_annTotal:annVPD_min)) %>%
# mutate(Lon = st_coordinates(.)[,1],
# Lat = st_coordinates(.)[,2]) %>%
# st_drop_geometry() %>%
# filter(!is.na(newRegion))
rename("tmin" = tmin_meanAnnAvg_CLIM,
"tmax" = tmax_meanAnnAvg_CLIM, #1
"tmean" = tmean_meanAnnAvg_CLIM,
"prcp" = prcp_meanAnnTotal_CLIM,
"t_warm" = T_warmestMonth_meanAnnAvg_CLIM,
"t_cold" = T_coldestMonth_meanAnnAvg_CLIM,
"prcp_wet" = precip_wettestMonth_meanAnnAvg_CLIM,
"prcp_dry" = precip_driestMonth_meanAnnAvg_CLIM,
"prcp_seasonality" = precip_Seasonality_meanAnnAvg_CLIM, #2
"prcpTempCorr" = PrecipTempCorr_meanAnnAvg_CLIM, #3
"abvFreezingMonth" = aboveFreezing_month_meanAnnAvg_CLIM,
"isothermality" = isothermality_meanAnnAvg_CLIM, #4
"annWatDef" = annWaterDeficit_meanAnnAvg_CLIM,
"annWetDegDays" = annWetDegDays_meanAnnAvg_CLIM,
"VPD_mean" = annVPD_mean_meanAnnAvg_CLIM,
"VPD_max" = annVPD_max_meanAnnAvg_CLIM, #5
"VPD_min" = annVPD_min_meanAnnAvg_CLIM, #6
"VPD_max_95" = annVPD_max_95percentile_CLIM,
"annWatDef_95" = annWaterDeficit_95percentile_CLIM,
"annWetDegDays_5" = annWetDegDays_5percentile_CLIM,
"frostFreeDays_5" = durationFrostFreeDays_5percentile_CLIM,
"frostFreeDays" = durationFrostFreeDays_meanAnnAvg_CLIM,
"soilDepth" = soilDepth, #7
"clay" = surfaceClay_perc,
"sand" = avgSandPerc_acrossDepth, #8
"coarse" = avgCoarsePerc_acrossDepth, #9
"carbon" = avgOrganicCarbonPerc_0_3cm, #10
"AWHC" = totalAvailableWaterHoldingCapacity,
## anomaly variables
tmean_anom = tmean_meanAnnAvg_3yrAnom, #15
tmin_anom = tmin_meanAnnAvg_3yrAnom, #16
tmax_anom = tmax_meanAnnAvg_3yrAnom, #17
prcp_anom = prcp_meanAnnTotal_3yrAnom, #18
t_warm_anom = T_warmestMonth_meanAnnAvg_3yrAnom, #19
t_cold_anom = T_coldestMonth_meanAnnAvg_3yrAnom, #20
prcp_wet_anom = precip_wettestMonth_meanAnnAvg_3yrAnom, #21
precp_dry_anom = precip_driestMonth_meanAnnAvg_3yrAnom, #22
prcp_seasonality_anom = precip_Seasonality_meanAnnAvg_3yrAnom, #23
prcpTempCorr_anom = PrecipTempCorr_meanAnnAvg_3yrAnom, #24
aboveFreezingMonth_anom = aboveFreezing_month_meanAnnAvg_3yrAnom, #25
isothermality_anom = isothermality_meanAnnAvg_3yrAnom, #26
annWatDef_anom = annWaterDeficit_meanAnnAvg_3yrAnom, #27
annWetDegDays_anom = annWetDegDays_meanAnnAvg_3yrAnom, #28
VPD_mean_anom = annVPD_mean_meanAnnAvg_3yrAnom, #29
VPD_min_anom = annVPD_min_meanAnnAvg_3yrAnom, #30
VPD_max_anom = annVPD_max_meanAnnAvg_3yrAnom, #31
VPD_max_95_anom = annVPD_max_95percentile_3yrAnom, #32
annWatDef_95_anom = annWaterDeficit_95percentile_3yrAnom, #33
annWetDegDays_5_anom = annWetDegDays_5percentile_3yrAnom , #34
frostFreeDays_5_anom = durationFrostFreeDays_5percentile_3yrAnom, #35
frostFreeDays_anom = durationFrostFreeDays_meanAnnAvg_3yrAnom #36
) %>%
dplyr::select(-c(tmin_meanAnnAvg_3yr:durationFrostFreeDays_meanAnnAvg_3yr))
The following are the candidate predictor variables for this ecoregion:
prednames <- c(
# "tmin" , "tmax" , "tmean" , "prcp" ,
# "t_warm" , "t_cold" , "prcp_wet" , "prcp_dry" ,
# "prcp_seasonality" , "prcpTempCorr" , "abvFreezingMonth", "isothermality" ,
# "annWatDef" , "annWetDegDays" , "VPD_mean" , "VPD_max" ,
# "VPD_min" , "VPD_max_95" , "annWatDef_95" , "annWetDegDays_5" ,
# "frostFreeDays_5" , "frostFreeDays" , "soilDepth" ,
# "clay" , "sand" , "coarse" , "carbon" ,
# "AWHC"
"tmean", "prcp", "prcp_seasonality", "prcpTempCorr", "isothermality", "annWetDegDays", "sand", "coarse", "AWHC"
)
print(prednames)
## [1] "tmean" "prcp" "prcp_seasonality" "prcpTempCorr"
## [5] "isothermality" "annWetDegDays" "sand" "coarse"
## [9] "AWHC"
allPreds <- modDat_1 %>%
dplyr::select(tmin:frostFreeDays,tmean_anom:frostFreeDays_anom, soilDepth:AWHC) %>%
names()
modDat_1_s <- modDat_1 %>%
mutate(across(all_of(allPreds), base::scale, .names = "{.col}_s"))
saveRDS(modDat_1_s, file = "./models/scaledModelInputData.rds")
# Remove the rows that have no observations for total tree cover
modDat_1_s <- modDat_1_s[!is.na(modDat_1_s[,"TotalTreeCover_binom"]),]
# subset the data to only include these predictors, and remove any remaining NAs
modDat_1_s <- modDat_1_s %>%
dplyr::select(prednames, paste0(prednames, "_s"), TotalTreeCover, TotalTreeCover_binom, newRegion, Year, x, y, NA_L1NAME, NA_L2NAME) %>%
drop_na()
names(prednames) <- prednames
df_pred <- modDat_1_s[, prednames]
response <- "TotalTreeCover"
ggplot(modDat_1_s) +
geom_histogram(aes(TotalTreeCover/100), fill = "darkgreen", col = "darkgreen", alpha = .5) +
xlab("Tree Cover") +
ggtitle("Untransformed, observed tree cover")
ggplot(modDat_1_s) +
geom_histogram(aes(TotalTreeCover_binom), fill = "purple", alpha = .5, col = "purple") +
xlab("Tree Cover") +
ggtitle(paste0("Tree cover, converted to binomial with a ",treeThreshold,"cutoff"))
create_summary <- function(df) {
df %>%
pivot_longer(cols = everything(),
names_to = 'variable') %>%
group_by(variable) %>%
summarise(across(value, .fns = list(mean = ~mean(.x, na.rm = TRUE), min = ~min(.x, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE), max = ~max(.x, na.rm = TRUE)))) %>%
mutate(across(where(is.numeric), round, 4))
}
modDat_1_s[prednames] %>%
create_summary() %>%
knitr::kable(caption = 'summaries of possible predictor variables') %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
| variable | value_mean | value_min | value_median | value_max |
|---|---|---|---|---|
| AWHC | 14.8464 | 0.0000 | 14.2490 | 35.2881 |
| annWetDegDays | 1854.5123 | 81.2108 | 1608.3989 | 7131.5166 |
| coarse | 9.3698 | 0.0000 | 5.4888 | 79.9649 |
| isothermality | 37.6823 | 19.4935 | 37.7050 | 63.7425 |
| prcp | 482.9681 | 47.6797 | 399.2635 | 4360.3490 |
| prcpTempCorr | 0.0474 | -0.8613 | 0.1236 | 0.7098 |
| prcp_seasonality | 0.9725 | 0.3568 | 0.9379 | 2.2319 |
| sand | 46.5733 | 0.0000 | 45.2442 | 99.8226 |
| tmean | 11.1518 | -2.2524 | 10.0167 | 24.9823 |
scaleFigDat_1 <- modDat_1_s %>%
dplyr::select(c(x, y, Year, prednames)) %>%
pivot_longer(cols = all_of(names(prednames)),
names_to = "predNames",
values_to = "predValues_unScaled")
scaleFigDat_2 <- modDat_1_s %>%
dplyr::select(c(x, y, Year, paste0(prednames, "_s"))) %>%
pivot_longer(cols = all_of(paste0(prednames,"_s"
)),
names_to = "predNames",
values_to = "predValues_scaled",
names_sep = ) %>%
mutate(predNames = str_replace(predNames, pattern = "_s$", replacement = ""))
scaleFigDat_3 <- scaleFigDat_1 %>%
left_join(scaleFigDat_2)
ggplot(scaleFigDat_3) +
facet_wrap(~predNames, scales = "free") +
geom_histogram(aes(predValues_unScaled), fill = "lightgrey", col = "darkgrey") +
geom_histogram(aes(predValues_scaled), fill = "lightblue", col = "blue") +
xlab ("predictor variable values") +
ggtitle("Comparing the distribution of unscaled (grey) to scaled (blue) predictor variables")
modDat_1_s <- modDat_1_s %>%
rename_with(~paste0(.x, "_raw"),
any_of(names(prednames))) %>%
rename_with(~str_remove(.x, "_s$"),
any_of(paste0(names(prednames), "_s")))
set.seed(12011993)
# vector of name of response variables
vars_response <- response
# longformat dataframes for making boxplots
df_sample_plots <- modDat_1_s %>%
slice_sample(n = 5e4) %>%
rename(response = all_of("TotalTreeCover_binom")) %>%
mutate(response = case_when(
response == 0 ~ "0",
response > 0 ~ "1",
)) %>%
dplyr::select(c(response, prednames)) %>%
tidyr::pivot_longer(cols = unname(prednames),
names_to = "predictor",
values_to = "value"
)
ggplot(df_sample_plots, aes_string(x= "response", y = 'value')) +
geom_boxplot() +
facet_wrap(~predictor , scales = 'free_y') +
ylab("Predictor Variable Values") +
xlab(response)
First, if there are observations in Louisiana, sub-sample them so they’re not so over-represented in the dataset
## make data into spatial format
modDat_1_sf <- modDat_1_s %>%
st_as_sf(coords = c("x", "y"), crs = st_crs("EPSG:4326"))
# download map info for visualization
data(state_boundaries_wgs84)
cropped_states <- suppressMessages(state_boundaries_wgs84 %>%
dplyr::filter(NAME!="Hawaii") %>%
dplyr::filter(NAME!="Alaska") %>%
dplyr::filter(NAME!="Puerto Rico") %>%
dplyr::filter(NAME!="American Samoa") %>%
dplyr::filter(NAME!="Guam") %>%
dplyr::filter(NAME!="Commonwealth of the Northern Mariana Islands") %>%
dplyr::filter(NAME!="United States Virgin Islands") %>%
sf::st_sf() %>%
sf::st_transform(sf::st_crs(modDat_1_sf)))
## do a pca of climate across level 2 ecoregions
pca <- prcomp(modDat_1_s[,paste0(prednames)])
library(factoextra)
(fviz_pca_ind(pca, habillage = modDat_1_s$NA_L2NAME, label = "none", addEllipses = TRUE, ellipse.level = .95, ggtheme = theme_minimal(), alpha.ind = .1))
# make a table of n for each region
modDat_1_s %>%
group_by(NA_L2NAME) %>%
dplyr::summarize("Number_Of_Observations" = length(NA_L2NAME)) %>%
rename("Level_2_Ecoregion" = NA_L2NAME)%>%
kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
| Level_2_Ecoregion | Number_Of_Observations |
|---|---|
| ATLANTIC HIGHLANDS | 1064 |
| CENTRAL USA PLAINS | 80 |
| COLD DESERTS | 170077 |
| EVERGLADES | 3 |
| MARINE WEST COAST FOREST | 2759 |
| MEDITERRANEAN CALIFORNIA | 13081 |
| MISSISSIPPI ALLUVIAL AND SOUTHEAST USA COASTAL PLAINS | 1583 |
| MIXED WOOD PLAINS | 1456 |
| MIXED WOOD SHIELD | 1226 |
| OZARK/OUACHITA-APPALACHIAN FORESTS | 2454 |
| SOUTH CENTRAL SEMIARID PRAIRIES | 110368 |
| SOUTHEASTERN USA PLAINS | 4428 |
| TAMAULIPAS-TEXAS SEMIARID PLAIN | 7401 |
| TEMPERATE PRAIRIES | 14776 |
| TEXAS-LOUISIANA COASTAL PLAIN | 4538 |
| UPPER GILA MOUNTAINS | 8379 |
| WARM DESERTS | 66844 |
| WEST-CENTRAL SEMIARID PRAIRIES | 86414 |
| WESTERN CORDILLERA | 43655 |
| WESTERN SIERRA MADRE PIEDMONT | 6844 |
map1 <- ggplot() +
geom_sf(data=cropped_states,fill='white') +
geom_sf(data=modDat_1_sf#[modDat_1_sf$NA_L2NAME %in% c("MIXED WOOD PLAINS"),]
,
aes(fill=as.factor(NA_L2NAME)),linewidth=0.5,alpha=0.5) +
geom_point(data=modDat_1_s#[modDat_1_s$NA_L2NAME %in% c("MIXED WOOD PLAINS"),]
,
alpha=0.5,
aes(x = x, y = y, color=as.factor(NA_L2NAME)), alpha = .1) +
#scale_fill_okabeito() +
#scale_color_okabeito() +
# theme_default() +
theme(legend.position = 'none') +
labs(title = "Level 2 Ecoregions as spatial blocks")
hull <- modDat_1_sf %>%
ungroup() %>%
group_by(NA_L2NAME) %>%
slice(chull(tmean, prcp))
plot1<-ggplot(data=modDat_1_sf,aes(x=tmean,y=prcp)) +
geom_polygon(data = hull, alpha = 0.25,aes(fill=NA_L2NAME) )+
geom_point(aes(group=NA_L2NAME,color=NA_L2NAME),alpha=0.25) +
theme_minimal() + xlab("Annual Average T_mean - long-term average") +
ylab("Annual Average Precip - long-term average") #+
#scale_color_okabeito() +
#scale_fill_okabeito()
plot2<-ggplot(data=modDat_1_sf %>%
pivot_longer(cols=tmean:prcp),
aes(x=value,group=name)) +
# geom_polygon(data = hull, alpha = 0.25,aes(fill=fold) )+
geom_density(aes(group=NA_L2NAME,fill=NA_L2NAME),alpha=0.25) +
theme_minimal() +
facet_wrap(~name,scales='free')# +
#scale_color_okabeito() +
#scale_fill_okabeito()
library(patchwork)
(combo <- (map1+plot1)/plot2)
#
# ggplot(data = modDat_1_s) +
# geom_density(aes(ShrubCover_transformed, col = NA_L2NAME)) +
# xlim(c(0,100))
if (run == TRUE) {
# set up custom folds
# get the ecoregions for training lambda
train_eco <- modDat_1_s$NA_L2NAME#[train]
# Fit model -----------------------------------------------
# specify leave-one-year-out cross-validation
my_folds <- as.numeric(as.factor(train_eco))
# set up parallel processing
library(doMC)
# this computer has 16 cores (parallel::detectCores())
registerDoMC(cores = 8)
fit <- cv.glmnet(
x = X[,2:ncol(X)],
y = y,
family = "binomial",
keep = FALSE,
alpha = 1, # 0 == ridge regression, 1 == lasso, 0.5 ~~ elastic net
lambda = lambdas,
relax = ifelse(response == "ShrubCover", yes = TRUE, no = FALSE),
#nlambda = 100,
type.measure="mse",
#penalty.factor = pen_facts,
foldid = my_folds,
#thresh = thresh,
standardize = FALSE, ## scales variables prior to the model sequence... coefficients are always returned on the original scale
parallel = TRUE
)
base::saveRDS(fit, paste0("../ModelFitting/models/yesOrNoTrees_globalLASSOmod_binomial_treeCutoff_",treeThreshold,".rds"))
best_lambda <- fit$lambda.min
# save the lambda for the most regularized model w/ an MSE that is still 1SE w/in the best lambda model
lambda_1SE <- fit$lambda.1se
# save the lambda for the most regularized model w/ an MSE that is still .5SE w/in the best lambda model
lambda_halfSE <- best_lambda + ((lambda_1SE - best_lambda)/2)
## Now, we need to do stability selection to ensure the coefficients that are being chosen with each lambda are stable
## stability selection for best lambda model
# setup params
p <- ncol(X[,2:ncol(X)]) # of parameters
n <- length(y) # of observations
n_iter <- 100 # number of subsamples
sample_frac <- 0.75 # fraction of data to subsample
lambda_val <- best_lambda # fixed lambda value (could be chosen via CV)
# Track selection
selection_counts_bestL <- matrix(0, nrow = p, ncol = 1)
for (i in 1:n_iter) {
# Subsample rows
sample_idx <- sample(1:n, size = floor(sample_frac * n), replace = FALSE)
X_sub <- X[sample_idx, ]
y_sub <- y[sample_idx]
# Fit Lasso model
fit_stab_i <- glmnet(x = X_sub[,2:ncol(X_sub)], y = y_sub,
family = "binomial",
alpha = 1, lambda = lambda_val, standardize = FALSE)
# Get non-zero coefficients (excluding intercept)
select_bestL <- which(as.vector(coef(fit_stab_i))[-1] != 0)
selection_counts_bestL[select_bestL] <- selection_counts_bestL[select_bestL] + 1
}
# Convert counts to selection probabilities (the probability that a variable is selected over 100 iterations)
selection_prob_bestL <- selection_counts_bestL / n_iter
selection_prob_bestL_df <- data.frame(
VariableNumber = paste0("X", 1:p),
VariableName = rownames(coef(fit_stab_i))[2:(p+1)],
SelectionProb = as.vector(selection_prob_bestL)
)
# get those variables that are selected in ≥70% of subsets (Meinshausen and Bühlmann, 2010)
bestLambda_coef <- selection_prob_bestL_df[selection_prob_bestL_df$SelectionProb>=.7, c("VariableName", "SelectionProb")]
#//////
# stability selection for 1se lambda model
lambda_val <- lambda_1SE # fixed lambda value (could be chosen via CV)
# Track selection
selection_counts_1seL <- matrix(0, nrow = p, ncol = 1)
for (i in 1:n_iter) {
# Subsample rows
sample_idx <- sample(1:n, size = floor(sample_frac * n), replace = FALSE)
X_sub <- X[sample_idx, ]
y_sub <- y[sample_idx]
# Fit Lasso model
fit_stab_i <- glmnet(x = X_sub[,2:ncol(X_sub)], y = y_sub,
family = "binomial",
alpha = 1, lambda = lambda_val, standardize = FALSE)
# Get non-zero coefficients (excluding intercept)
selected_1seL <- which(as.vector(coef(fit_stab_i))[-1] != 0)
selection_counts_1seL[selected_1seL] <- selection_counts_1seL[selected_1seL] + 1
}
# Convert counts to selection probabilities (the probability that a variable is selected over 100 iterations)
selection_prob_1seL <- selection_counts_1seL / n_iter
selection_prob_1seL_df <- data.frame(
VariableNumber = paste0("X", 1:p),
VariableName = rownames(coef(fit_stab_i))[2:(p+1)],
SelectionProb = as.vector(selection_prob_1seL)
)
# get those variables that are selected in ≥70% of subsets (Meinshausen and Bühlmann, 2010)
seLambda_coef <- selection_prob_1seL_df[selection_prob_1seL_df$SelectionProb>=.7, c("VariableName", "SelectionProb")]
# stability selection for half se lambda model
lambda_val <- lambda_halfSE # fixed lambda value (could be chosen via CV)
# Track selection
selection_counts_halfseL <- matrix(0, nrow = p, ncol = 1)
for (i in 1:n_iter) {
# Subsample rows
sample_idx <- sample(1:n, size = floor(sample_frac * n), replace = FALSE)
X_sub <- X[sample_idx, ]
y_sub <- y[sample_idx]
# Fit Lasso model
fit_stab_i <- glmnet(x = X_sub[,2:ncol(X_sub)], y = y_sub,
family = "binomial",
alpha = 1, lambda = lambda_val, standardize = FALSE)
# Get non-zero coefficients (excluding intercept)
selected_halfseL <- which(as.vector(coef(fit_stab_i))[-1] != 0)
selection_counts_halfseL[selected_halfseL] <- selection_counts_halfseL[selected_halfseL] + 1
}
# Convert counts to selection probabilities (the probability that a variable is selected_halfseL over 100 iterations)
selection_prob_halfseL <- selection_counts_halfseL / n_iter
selection_prob_halfseL_df <- data.frame(
VariableNumber = paste0("X", 1:p),
VariableName = rownames(coef(fit_stab_i))[2:(p+1)],
SelectionProb = as.vector(selection_prob_halfseL)
)
# get those variables that are selected_halfseL_halfseL in ≥70% of subsets (Meinshausen and Bühlmann, 2010)
halfseLambda_coef <- selection_prob_halfseL_df[selection_prob_halfseL_df$SelectionProb>=.7, c("VariableName", "SelectionProb")]
## fit w/ the identified coefficients from the 'best' lambda, but using the glm function
mat_glmnet_best <- bestLambda_coef$VariableName
if (length(mat_glmnet_best) == 0) {
f_glm_bestLambda <- as.formula(paste0("TotalTreeCover_binom", "~ 1"))
} else {
f_glm_bestLambda <- as.formula(paste0("TotalTreeCover_binom", " ~ ", paste0(mat_glmnet_best, collapse = " + ")))
}
## fit using betareg
fit_glm_bestLambda <- fit_glm_bestLambda_binomial <- glm(formula = f_glm_bestLambda, data = modDat_1_s, family = binomial)
## fit w/ the identified coefficients from the '1se' lambda, but using the glm function
mat_glmnet_1se <- seLambda_coef$VariableName
if (length(mat_glmnet_1se) == 0) {
f_glm_1se <- as.formula(paste0("TotalTreeCover_binom", "~ 1"))
} else {
f_glm_1se <- as.formula(paste0("TotalTreeCover_binom", " ~ ", paste0(mat_glmnet_1se, collapse = " + ")))
}
fit_glm_se <- glm(formula = f_glm_1se, data = modDat_1_s, family = binomial)
# glm(data = modDat_1_s, formula = f_glm_1se,
# family = stats::Gamma(link = "log"))
## fit w/ the identified coefficients from the '.5se' lambda, but using the glm function
mat_glmnet_halfse <- halfseLambda_coef$VariableName
if (length(mat_glmnet_halfse) == 0) {
f_glm_halfse <- as.formula(paste0("TotalTreeCover_binom", "~ 1"))
} else {
f_glm_halfse <- as.formula(paste0("TotalTreeCover_binom", " ~ ", paste0(mat_glmnet_halfse, collapse = " + ")))
}
fit_glm_halfse <- glm(formula = f_glm_halfse, data = modDat_1_s, family = binomial )
## save models
saveRDS(fit_glm_bestLambda, paste0("./models/yesOrNoTrees_bestLambdaGLM_",treeThreshold,".rds"))
saveRDS(fit_glm_halfse, paste0("./models/yesOrNoTrees_halfSELambdaGLM_",treeThreshold,".rds"))
saveRDS(fit_glm_se, paste0("./models/yesOrNoTrees_oneSELambdaGLM_",treeThreshold,".rds"))
## save the R environment after running the models
save(f_glm_halfse, mat_glmnet_halfse, halfseLambda_coef,
f_glm_1se, mat_glmnet_1se, seLambda_coef,
f_glm_bestLambda, mat_glmnet_best, bestLambda_coef,
file = paste0("./models/interimModelFittingObjects_yesOrNoTrees_binomial_",treeThreshold,".rds"))
} else {
# read in LASSO object
fit <- readRDS(paste0("../ModelFitting/models/yesOrNoTrees_globalLASSOmod_binomial_treeCutoff_",treeThreshold,".rds"))
# read in R objects having to do w/ model fitting
load(file = paste0("./models/interimModelFittingObjects_yesOrNoTrees_binomial_",treeThreshold,".rds"))
fit_glm_bestLambda <- readRDS(paste0("./models/yesOrNoTrees_bestLambdaGLM_",treeThreshold,".rds"))
fit_glm_halfse <- readRDS(paste0("./models/yesOrNoTrees_halfSELambdaGLM_",treeThreshold,".rds"))
fit_glm_se <- readRDS(paste0("./models/yesOrNoTrees_oneSELambdaGLM_",treeThreshold,".rds"))
}
# assess model fit
# assess.glmnet(fit$fit.preval, #newx = X[,2:293],
# newy = y, family = stats::Gamma(link = "log"))
# save the minimum lambda
best_lambda <- fit$lambda.min
# save the lambda for the most regularized model w/ an MSE that is still 1SE w/in the best lambda model
lambda_1SE <- fit$lambda.1se
# save the lambda for the most regularized model w/ an MSE that is still .5SE w/in the best lambda model
lambda_halfSE <- best_lambda + ((lambda_1SE - best_lambda)/2)
print(fit)
##
## Call: cv.glmnet(x = X[, 2:ncol(X)], y = y, lambda = lambdas, type.measure = "mse", foldid = my_folds, keep = FALSE, parallel = TRUE, relax = ifelse(response == "ShrubCover", yes = TRUE, no = FALSE), family = "binomial", alpha = 1, standardize = FALSE)
##
## Measure: Mean-Squared Error
##
## Lambda Index Measure SE Nonzero
## min 0.01110 99 0.1563 0.03266 10
## 1se 0.08907 69 0.1868 0.04118 1
plot(fit)
## predict on the test data
# lasso model predictions with the optimal lambda
optimal_pred <- predict(fit_glm_bestLambda, newx=X[,2:ncol(X)], type = "response")
optimal_pred_1se <- predict(fit_glm_se, newx=X[,2:ncol(X)], type = "response")
optimal_pred_halfse <- predict(fit_glm_halfse, newx = X[,2:ncol(X)], type = "response")
null_fit <- glm(
formula = y ~ 1, #data = modDat_1_s,
family = binomial
)
null_pred <- predict(null_fit, newdata = as.data.frame(X), type = "response"
)
# save data
fullModOut <- list(
"modelObject" = fit,
"nullModelObject" = null_fit,
"modelPredictions" = data.frame(#ecoRegion_holdout = rep(test_eco,length(y)),
obs=y,
pred_opt=optimal_pred,
pred_opt_se = optimal_pred_1se,
pred_opt_halfse = optimal_pred_halfse,
pred_null=null_pred#,
#pred_nopenalty=nopen_pred
))
ggplot() +
geom_point(aes(X[,2], fullModOut$modelPredictions$obs), col = "black", alpha = .1) +
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_opt), col = "red", alpha = .1) + ## predictions w/ the CV model
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_opt_halfse), col = "orange", alpha = .1) + ## predictions w/ the CV model (.5se lambda)
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_opt_se), col = "green", alpha = .1) + ## predictions w/ the CV model (1se lambda)
geom_point(aes(X[,2], fullModOut$modelPredictions$pred_null), col = "blue", alpha = .1) +
labs(title = "A rough comparison of observed and model-predicted values",
subtitle = "black = observed values \n red = predictions from 'best lambda' model \n orange = predictions for '1/2se' lambda model \n green = predictions from '1se' lambda model \n blue = predictions from null model") +
xlab(colnames(X)[2])
#ylim(c(0,200))
r{print(best_lambda)}. The lambda value such that the cross
validation error is within 1 standard error of the minimum (“1se
lambda”) was `r{print(fit$lambda.1se)}`` . The following coefficients
were kept in each model:# the coefficient matrix from the 'best model' -- find and print those coefficients that aren't 0 in a table
coef_glm_bestLambda <- coef(fit_glm_bestLambda) %>%
data.frame()
coef_glm_bestLambda$coefficientName <- rownames(coef_glm_bestLambda)
names(coef_glm_bestLambda)[1] <- "coefficientValue_bestLambda"
# coefficient matrix from the '1se' model
coef_glm_1se <- coef(fit_glm_se) %>%
data.frame()
coef_glm_1se$coefficientName <- rownames(coef_glm_1se)
names(coef_glm_1se)[1] <- "coefficientValue_1seLambda"
# coefficient matrix from the 'half se' model
coef_glm_halfse <- coef(fit_glm_halfse) %>%
data.frame()
coef_glm_halfse$coefficientName <- rownames(coef_glm_halfse)
names(coef_glm_halfse)[1] <- "coefficientValue_halfseLambda"
# add together
coefs <- full_join(coef_glm_bestLambda, coef_glm_halfse) %>%
full_join(coef_glm_1se) %>%
dplyr::select(coefficientName, coefficientValue_bestLambda,
coefficientValue_halfseLambda, coefficientValue_1seLambda)
globModTerms <- coefs[!is.na(coefs$coefficientValue_bestLambda), "coefficientName"]
## also, get the number of unique variables in each model
var_prop_pred <- paste0(response, "_pred")
response_vars <- c(response, var_prop_pred)
# for best lambda model
prednames_fig <- paste(str_split(globModTerms, ":", simplify = TRUE))
prednames_fig <- str_replace(prednames_fig, "I\\(", "")
prednames_fig <- str_replace(prednames_fig, "\\^2\\)", "")
prednames_fig <- unique(prednames_fig[prednames_fig>0])
prednames_fig <- prednames_fig
prednames_fig_num <- length(prednames_fig)
# for 1SE lambda model
globModTerms_1se <- coefs[!is.na(coefs$coefficientValue_1seLambda), "coefficientName"]
if (length(globModTerms_1se) == 1) {
prednames_fig_1se <- paste(str_split(globModTerms_1se, ":", simplify = TRUE))
prednames_fig_1se <- str_replace(prednames_fig_1se, "I\\(", "")
prednames_fig_1se <- str_replace(prednames_fig_1se, "\\^2\\)", "")
prednames_fig_1se <- unique(prednames_fig_1se[prednames_fig_1se>0])
prednames_fig_1se_num <- c(0)
} else {
prednames_fig_1se <- paste(str_split(globModTerms_1se, ":", simplify = TRUE))
prednames_fig_1se <- str_replace(prednames_fig_1se, "I\\(", "")
prednames_fig_1se <- str_replace(prednames_fig_1se, "\\^2\\)", "")
prednames_fig_1se <- unique(prednames_fig_1se[prednames_fig_1se>0])
prednames_fig_1se_num <- length(prednames_fig_1se)
}
# for 1/2SE lambda model
globModTerms_halfse <- coefs[!is.na(coefs$coefficientValue_halfseLambda), "coefficientName"]
if (length(globModTerms_halfse) == 1) {
prednames_fig_halfse <- paste(str_split(globModTerms_halfse, ":", simplify = TRUE))
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "I\\(", "")
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "\\^2\\)", "")
prednames_fig_halfse <- unique(prednames_fig_halfse[prednames_fig_halfse>0])
prednames_fig_halfse_num <- c(0)
} else {
prednames_fig_halfse <- paste(str_split(globModTerms_halfse, ":", simplify = TRUE))
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "I\\(", "")
prednames_fig_halfse <- str_replace(prednames_fig_halfse, "\\^2\\)", "")
prednames_fig_halfse <- unique(prednames_fig_halfse[prednames_fig_halfse>0])
prednames_fig_halfse_num <- length(prednames_fig_halfse)
}
# make a table
kable(coefs, col.names = c("Coefficient Name", "Value from best lambda model",
"Value from 1/2 se lambda", "Value from 1se lambda model")
) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
| Coefficient Name | Value from best lambda model | Value from 1/2 se lambda | Value from 1se lambda model |
|---|---|---|---|
| (Intercept) | -2.4534828 | -2.381544 | -2.381544 |
| prcp | 1.7702591 | 1.523568 | 1.523568 |
| prcp_seasonality | -0.8341143 | NA | NA |
| coarse | 0.2005033 | NA | NA |
| AWHC | -0.8560206 | NA | NA |
| I(tmean^2) | -0.2958276 | NA | NA |
| I(prcp_seasonality^2) | 0.0845484 | NA | NA |
| I(isothermality^2) | 0.0492120 | NA | NA |
| I(sand^2) | -0.3505392 | NA | NA |
| prcpTempCorr:isothermality | -0.2580872 | NA | NA |
# calculate RMSE of all models
RMSE_best <- yardstick::rmse(fullModOut$modelPredictions[,c("obs", "pred_opt")], truth = "obs", estimate = "pred_opt")$.estimate
RMSE_halfse <- yardstick::rmse(fullModOut$modelPredictions[,c("obs", "pred_opt_halfse")], truth = "obs", estimate = "pred_opt_halfse")$.estimate
RMSE_1se <- yardstick::rmse(fullModOut$modelPredictions[,c("obs", "pred_opt_se")], truth = "obs", estimate = "pred_opt_se")$.estimate
# calculate bias of all models
bias_best <- mean((fullModOut$modelPredictions$obs) - fullModOut$modelPredictions$pred_opt)
bias_halfse <- mean((fullModOut$modelPredictions$obs) - fullModOut$modelPredictions$pred_opt_halfse)
bias_1se <- mean((fullModOut$modelPredictions$obs) - fullModOut$modelPredictions$pred_opt_se)
uniqueCoeffs <- data.frame("Best lambda model" = c(signif(RMSE_best,3), as.character(signif(bias_best, 3)),
as.integer(length(globModTerms)-1), as.integer(prednames_fig_num),
as.integer(sum(prednames_fig %in% c(prednames_clim))),
as.integer(sum(prednames_fig %in% c(prednames_weath))),
as.integer(sum(prednames_fig %in% c(prednames_soils)))
),
"1/2 se lambda model" = c(signif(RMSE_halfse,3), as.character(signif(bias_halfse, 3)),
length(globModTerms_halfse)-1, prednames_fig_halfse_num,
sum(prednames_fig_halfse %in% c(prednames_clim)),
sum(prednames_fig_halfse %in% c(prednames_weath)),
sum(prednames_fig_halfse %in% c(prednames_soils))),
"1se lambda model" = c(signif(RMSE_1se,3), as.character(signif(bias_1se, 3)),
length(globModTerms_1se)-1, prednames_fig_1se_num,
sum(prednames_fig_1se %in% c(prednames_clim)),
sum(prednames_fig_1se %in% c(prednames_weath)),
sum(prednames_fig_1se %in% c(prednames_soils))))
row.names(uniqueCoeffs) <- c("RMSE", "bias: mean(obs-pred.)", "Total number of coefficients", "Number of unique coefficients",
"Number of unique climate coefficients",
"Number of unique weather coefficients",
"Number of unique soils coefficients"
)
kable(uniqueCoeffs,
col.names = c("Best lambda model", "1/2 se lambda model", "1se lambda model"), row.names = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
| Best lambda model | 1/2 se lambda model | 1se lambda model | |
|---|---|---|---|
| RMSE | 0.262 | 0.279 | 0.279 |
| bias: mean(obs-pred.) | -6.28e-11 | -1.11e-09 | -1.11e-09 |
| Total number of coefficients | 9 | 1 | 1 |
| Number of unique coefficients | 8 | 1 | 1 |
| Number of unique climate coefficients | 5 | 1 | 1 |
| Number of unique weather coefficients | 0 | 0 | 0 |
| Number of unique soils coefficients | 3 | 0 | 0 |
In the following figures, we show model predictions made using the best lambda model, as well as an alternative “second-best” lambda model. As the alternative to the best lambda model, we use the model (1se or 1/2se of best Lambda) that has the fewest number of unique predictors, but is not a null model.
if (whichSecondBestMod == "auto") {
# name of model w/ fewest # of predictors (but more than 0)
uniqueCoeff_min <- min(as.numeric(uniqueCoeffs[4,2:3])[which(as.numeric(uniqueCoeffs[4,2:3]) > 0)])
alternativeModel <- names(uniqueCoeffs[4,2:3])[which(uniqueCoeffs[4,2:3] == uniqueCoeff_min)]
if (is.finite(uniqueCoeff_min)) {
if (length(alternativeModel) == 1) {
if (alternativeModel == "X1.2.se.lambda.model") {
mod_secondBest <- fit_glm_halfse
name_secondBestMod <- "1/2 SE Model"
prednames_secondBestMod <- prednames_fig_halfse
} else if (alternativeModel == "X1se.lambda.model") {
mod_secondBest <- fit_glm_se
name_secondBestMod <- "1 SE Model"
prednames_secondBestMod <- prednames_fig_1se
}
} else {
# if both alternative models have the same number of unique coefficients, chose the model that has the fewest number of total predictors
uniqueCoeff_min2 <- min(as.numeric(uniqueCoeffs[3,alternativeModel]))
alternativeModel2 <- names(uniqueCoeffs[3,alternativeModel])[which(uniqueCoeffs[3,alternativeModel] == uniqueCoeff_min2)]
if (length(alternativeModel2) == 1) {
if (alternativeModel2 == "X1.2.se.lambda.model") {
mod_secondBest <- fit_glm_halfse
name_secondBestMod <- "1/2 SE Model"
prednames_secondBestMod <- prednames_fig_halfse
} else if (alternativeModel2 == "X1se.lambda.model") {
mod_secondBest <- fit_glm_se
name_secondBestMod <- "1 SE Model"
prednames_secondBestMod <- prednames_fig_1se
}
} else {
mod_secondBest <- fit_glm_halfse
name_secondBestMod <- "1/2 SE Model"
prednames_secondBestMod <- prednames_fig_halfse
}
}
}else {
mod_secondBest <- NULL
name_secondBestMod <- "Intercept_Only"
prednames_secondBestMod <- NULL
}
} else if (whichSecondBestMod == "1se") {
mod_secondBest <- fit_glm_se
name_secondBestMod <- "1 SE Model"
prednames_secondBestMod <- prednames_fig_1se
} else if (whichSecondBestMod == "halfse") {
mod_secondBest <- fit_glm_halfse
name_secondBestMod <- "1/2 SE Model"
prednames_secondBestMod <- prednames_fig_halfse
}
# create prediction for each each model
# (i.e. for each fire proporation variable)
predict_by_response <- function(mod, df) {
df_out <- df
response_name <- paste0("TotalTreeCover_binom", "_pred")
preds <- predict(mod, newx= df_out, #s="lambda.min",
type = "response")
preds[preds<0] <- 0
#preds[preds>100] <- 100
df_out <- df_out %>% cbind(preds)
colnames(df_out)[ncol(df_out)] <- response_name
return(df_out)
}
pred_glm1 <- predict_by_response(fit_glm_bestLambda, X[,2:ncol(X)])
## back-transform the
# add back in true y values
pred_glm1 <- pred_glm1 %>%
cbind(data.frame("TotalTreeCover_binom" = modDat_1_s$TotalTreeCover_binom,
"TotalTreeCover" = modDat_1_s$TotalTreeCover))
# add back in lat/long data
pred_glm1 <- pred_glm1 %>%
cbind(modDat_1_s[,c("x", "y", "Year")])
pred_glm1$resid <- pred_glm1[,"TotalTreeCover_binom"] - pred_glm1[,paste0("TotalTreeCover_binom", "_pred")]
pred_glm1$extremeResid <- NA
pred_glm1[pred_glm1$resid > .5 | pred_glm1$resid < -.5,"extremeResid"] <- 1
# "binomialize" the continuouse predictions
pred_glm1 <- pred_glm1 %>%
mutate(TotalTreeCover_binom_pred_rounded = round(TotalTreeCover_binom_pred))
if ( name_secondBestMod == "Intercept_Only") {
print("The next best lambda model only contains one predictor (an intercept)")
} else {
pred_glm1_1se <- predict_by_response(mod_secondBest, X[,2:ncol(X)])
# add back in true y values
pred_glm1_1se <- pred_glm1_1se %>%
cbind(data.frame("TotalTreeCover_binom" = modDat_1_s$TotalTreeCover_binom,
"TotalTreeCover" = modDat_1_s$TotalTreeCover))
# add back in lat/x data
pred_glm1_1se <- pred_glm1_1se %>%
cbind(modDat_1_s[,c("x", "y", "Year")])
pred_glm1_1se$resid <- pred_glm1_1se[,"TotalTreeCover_binom"] - pred_glm1_1se[,paste0("TotalTreeCover_binom", "_pred")]
pred_glm1_1se$extremeResid <- NA
pred_glm1_1se[pred_glm1_1se$resid > .5 | pred_glm1_1se$resid < -.5,"extremeResid"] <- 1
# "binomialize" the continuouse predictions
pred_glm1_1se <- pred_glm1_1se %>%
mutate(TotalTreeCover_binom_pred_rounded = round(TotalTreeCover_binom_pred))
}
# rasterize
# get reference raster
# test_rast <- rast("../../../Data_raw/dayMet/rawMonthlyData/orders/70e0da02b9d2d6e8faa8c97d211f3546/Daymet_Monthly_V4R1/data/daymet_v4_prcp_monttl_na_1980.tif") %>%
# #terra::aggregate(fact = 3, fun = "mean") %>%
# terra::project(crs("EPSG:4326"))
# transform to match format of veg. data
## add ecoregion boundaries (for our ecoregion level model)
regions <- sf::st_read(dsn = "../../../Data_raw/Level2Ecoregions/", layer = "NA_CEC_Eco_Level2")
## Reading layer `NA_CEC_Eco_Level2' from data source
## `/Users/astears/Documents/Dropbox_static/Work/NAU_USGS_postdoc/cleanPED/PED_vegClimModels/Data_raw/Level2Ecoregions'
## using driver `ESRI Shapefile'
## Simple feature collection with 2261 features and 8 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -4334052 ymin: -3313739 xmax: 3324076 ymax: 4267265
## Projected CRS: Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area
regions <- regions %>%
st_transform(crs = st_crs(crs("EPSG:4326"))) %>%
st_make_valid()
ecoregionLU <- data.frame("NA_L1NAME" = sort(unique(regions$NA_L1NAME)),
"newRegion" = c(NA, "Forest", "dryShrubGrass",
"dryShrubGrass", "Forest", "dryShrubGrass",
"dryShrubGrass", "Forest", "Forest",
"dryShrubGrass", "Forest", "Forest",
"Forest", "Forest", "dryShrubGrass",
NA
))
goodRegions <- regions %>%
left_join(ecoregionLU)
mapRegions <- goodRegions %>%
filter(!is.na(newRegion)) %>%
group_by(newRegion) %>%
summarise(geometry = sf::st_union(geometry)) %>%
ungroup() %>%
st_simplify(dTolerance = 1000) %>%
st_crop(ext(-130, -60, 20, 60))
# rasterize data
plotObs <- pred_glm1 %>%
drop_na("TotalTreeCover_binom") #%>%
# sf::st_as_sf(coords = c("x", "y"), remove = FALSE) %>%
# sf::st_set_crs(crs(test_rast)) #%>%
#slice_sample(n = 5e4) %>%
#terra::vect(geom = c("x", "y")) %>%
#terra::set.crs(crs(test_rast)) #%>%
# terra::rasterize(y = test_rast,
# field = "TotalTreeCover_binom",
# fun = function(x) {
# round(mean(x, na.rm = TRUE))
# }) %>%
# terra::crop(ext(-130, -60, 20, 60))
# make shapefile of cropped state boundaries in appropriate crs
cropped_states_2 <- cropped_states %>%
st_transform(crs = "EPSG:4326") %>%
st_make_valid() %>%
st_crop(ext(-130, -60, 20, 60))
# make figure of raw tree cover
map_obs_cont <- ggplot() +
#geom_spatraster(data = plotObs) +
#geom_sf(data = plotObs, aes(col = TotalTreeCover_binom)) +
stat_summary_2d(data = plotObs, aes(x = x, y = y, z = TotalTreeCover), fun = mean, binwidth = .05) +
geom_sf(data=cropped_states_2 ,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Observations of Continuous Total Tree Cover")) +
scale_fill_gradient2(low = "brown",
mid = "wheat" ,
high = "darkgreen" ,
midpoint = 0, na.value = "darkgrey") +
xlim(-125, -65) +
ylim(25, 50)
# make figure of binomialized tree cover
map_obs <- ggplot() +
#geom_spatraster(data = plotObs) +
#geom_sf(data = plotObs, aes(col = TotalTreeCover_binom)) +
stat_summary_2d(data = plotObs, aes(x = x, y = y, z = TotalTreeCover_binom), fun = mean, binwidth = .05) +
scale_fill_viridis_c(option = "A", guide = guide_colorbar(title = "% cover"),
limits = c(0,100)) +
geom_sf(data=cropped_states_2 ,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Observations of Binomial Total Tree Cover")) +
scale_fill_gradient2(low = "brown",
mid = "wheat" ,
high = "darkgreen" ,
midpoint = 0, na.value = "darkgrey") +
xlim(-125, -65) +
ylim(25, 50)
hist_obs_cont <- ggplot(pred_glm1) +
geom_histogram(aes(TotalTreeCover), fill = "lightgrey", col = "darkgrey")
hist_obs <- ggplot(pred_glm1) +
geom_histogram(aes(TotalTreeCover_binom), fill = "lightgrey", col = "darkgrey")
library(ggpubr)
ggarrange(map_obs_cont, hist_obs_cont, map_obs, hist_obs, heights = c(3, 1, 3, 1), ncol = 1)
#
# # rasterize data
# plotPred <- pred_glm1 %>%
# drop_na(paste0("TotalTreeCover_binom","_pred")) %>%
# #slice_sample(n = 5e4) %>%
# sf::st_as_sf(coords = c("x", "y"), remove = FALSE) %>%
# sf::st_set_crs(crs(test_rast))
# # terra::rasterize(y = test_rast,
# # field = paste0("TotalTreeCover_binom","_pred"),
# # fun = mean) %>%
# # terra::crop(ext(-130, -60, 20, 60))
#
# make figure - continuous predictions
map_preds1_cont <- ggplot() +
#geom_spatraster(data = plotPred) +
stat_summary_2d(data = plotObs, aes(x = x, y = y, z = TotalTreeCover_binom_pred), fun = mean, binwidth = .05) +
geom_sf(data=cropped_states_2,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Predictions from the 'best lambda' fitted model of Yes/No Trees \ncontinuous probabilities"),
subtitle = "bestLambda model") +
scale_fill_gradient2(low = "wheat",
mid = "darkgreen",
high = "red" ,
midpoint = 1, na.value = "darkgrey",
limits = c(0,1)) +
xlim(-125, -65) +
ylim(25, 50)
# make figure - binomialized predictions
map_preds1 <- ggplot() +
#geom_spatraster(data = plotPred) +
stat_summary_2d(data = plotObs, aes(x = x, y = y, z = TotalTreeCover_binom_pred_rounded), fun = function(x) {round(mean(x))}, binwidth = .05) +
geom_sf(data=cropped_states_2,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Predictions from the 'best lambda' fitted model of Yes/No Trees - \nbinomialized"),
subtitle = "bestLambda model") +
scale_fill_gradient2(low = "wheat",
mid = "darkgreen",
high = "red" ,
midpoint = 1, na.value = "darkgrey",
limits = c(0,1),
breaks = c(0,1)) +
xlim(-125, -65) +
ylim(25, 50)
hist_preds1_cont <- ggplot(pred_glm1) +
geom_histogram(aes(.data[[paste0("TotalTreeCover_binom", "_pred")]]), fill = "lightgrey", col = "darkgrey")
hist_preds1 <- ggplot(pred_glm1) +
geom_histogram(aes(x = TotalTreeCover_binom_pred_rounded), fill = "lightgrey", col = "darkgrey")#+
#xlim(c(-.01,1.01))
ggarrange(map_preds1_cont, hist_preds1_cont, map_preds1, hist_preds1, heights = c(3,1,3,1), ncol = 1)
if ( name_secondBestMod == "Intercept_Only") {
print("The next best lambda model only contains one predictor (an intercept)")
} else {
# rasterize data
plotPred <- pred_glm1_1se %>%
drop_na(paste0("TotalTreeCover_binom","_pred")) #%>%
# #slice_sample(n = 5e4) %>%
# sf::st_as_sf(coords = c("x", "y"), remove = FALSE) %>%
# sf::st_set_crs(crs(test_rast))
# terra::vect(geom = c("x", "y")) %>%
# terra::set.crs(crs(test_rast)) %>%
# terra::rasterize(y = test_rast,
# field = paste0("TotalTreeCover_binom","_pred"),
# fun = mean) %>%
# terra::crop(ext(-130, -60, 20, 60))
# make figures
map_preds2_cont <- ggplot() +
stat_summary_2d(data = plotPred, aes(x = x, y = y, z = TotalTreeCover_binom_pred), fun = mean, binwidth = .05) +
#geom_spatraster(data = plotPred) +
geom_sf(data=cropped_states_2,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Predictions from the ", name_secondBestMod, " of Yes/No Trees \ncontinuous probabilities"),
subtitle = name_secondBestMod) +
scale_fill_gradient2(low = "wheat",
mid = "darkgreen",
high = "red" ,
midpoint = 1, na.value = "darkgrey",
limits = c(0, 1)) +
xlim(-125, -65) +
ylim(25, 50)
# make figure - binomialized predictions
map_preds2 <- ggplot() +
#geom_spatraster(data = plotPred) +
stat_summary_2d(data = plotPred, aes(x = x, y = y, z = TotalTreeCover_binom_pred_rounded), fun = function(x) {round(mean(x))}, binwidth = .05) +
geom_sf(data=cropped_states_2,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
labs(title = paste0("Predictions from the ", name_secondBestMod, " of Yes/No Trees - \nbinomialized"),
subtitle = name_secondBestMod) +
scale_fill_gradient2(low = "wheat",
mid = "darkgreen",
high = "red" ,
midpoint = 1, na.value = "darkgrey",
limits = c(0,1),
breaks = c(0,1)) +
xlim(-125, -65) +
ylim(25, 50)
hist_preds2_cont <- ggplot(pred_glm1_1se) +
geom_histogram(aes(.data[[paste0("TotalTreeCover_binom", "_pred")]]), fill = "lightgrey", col = "darkgrey")
hist_preds2 <- ggplot(pred_glm1_1se) +
geom_histogram(aes(x = TotalTreeCover_binom_pred_rounded), fill = "lightgrey", col = "darkgrey")
ggarrange(map_preds2_cont, hist_preds2_cont, map_preds2, hist_preds2, heights = c(3,1,3,1), ncol = 1)
}
## calculate the classification error (binomial obs - binomial pred)
pred_glm1 <- pred_glm1 %>%
mutate(missClass = TotalTreeCover_binom - TotalTreeCover_binom_pred_rounded)
# make figures
(map_missClass <- ggplot() +
#geom_spatraster(data =plotResid_rast) +
stat_summary_2d(data = pred_glm1, aes(x = x, y = y, z = missClass), fun = function(x) {round(mean(x))}, binwidth = .05) +
geom_sf(data=cropped_states_2,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
#geom_sf(data = badResids_high, col = "blue") +
#geom_sf(data = badResids_low, col = "red") +
labs(title = paste0("Missclassification (obs. - pred.) from the model of Yes/No Trees"),
subtitle = "bestLambda model \nbinomialized observations - binomial predictions") +
scale_fill_gradient2(low = "red",
mid = "white" ,
high = "blue" ,
midpoint = 0, na.value = "darkgrey",
limits = c(-1,1),
breaks = c(-1,0,1),
labels = c("Data = no trees; Model = trees ", "aggree",
"Data = trees; Model = no trees")
) +
xlim(-125, -65) +
ylim(25, 50))
# make a confusion matrix
# prepare data
matData <- pred_glm1 %>%
mutate(predClass = TotalTreeCover_binom_pred_rounded,
obsClass = TotalTreeCover_binom) %>%
mutate(predClass = replace(predClass, predClass == 0, "no trees"),
predClass = replace(predClass, predClass == 1, "trees"),
obsClass = replace(obsClass, obsClass == 0, "no trees"),
obsClass = replace(obsClass, obsClass == 1, "trees")) %>%
mutate(predClass = as.factor(predClass) ,
obsClass = as.factor(obsClass))
# make matrix as a data.frame
# confMat <- confusionMatrix(data = matData$predClass,
# reference = matData$obsClass)
ConfusionTableR::binary_visualiseR(
train_labels = as.factor(matData$predClass),
truth_labels = as.factor(matData$obsClass),
class_label1 = "No trees",
class_label2 = "Trees",
quadrant_col1 = "wheat",
quadrant_col2 = "darkgreen",
text_col = "white"
)
## calculate the classification error (binomial obs - binomial pred)
pred_glm1_1se <- pred_glm1_1se %>%
mutate(missClass = TotalTreeCover_binom - TotalTreeCover_binom_pred_rounded)
# make figures
map_missClass <- ggplot() +
#geom_spatraster(data =plotResid_rast) +
stat_summary_2d(data = pred_glm1_1se, aes(x = x, y = y, z = missClass), fun = function(x) {round(mean(x))}, binwidth = .05) +
geom_sf(data=cropped_states_2,fill=NA ) +
geom_sf(data = mapRegions, fill = NA, col = "orchid", lwd = .5) +
#geom_sf(data = badResids_high, col = "blue") +
#geom_sf(data = badResids_low, col = "red") +
labs(title = paste0("Missclassification (obs. - pred.) from the model of Yes/No Trees"),
subtitle = paste0(name_secondBestMod, "\nbinomialized observations - binomial predictions")) +
scale_fill_gradient2(low = "red",
mid = "white" ,
high = "blue" ,
midpoint = 0, na.value = "darkgrey",
limits = c(-1,1),
breaks = c(-1,0,1),
labels = c("Data = no trees; Model = trees ", "aggree",
"Data = trees; Model = no trees")
) +
xlim(-125, -65) +
ylim(25, 50)
map_missClass
# make a confusion matrix
# prepare data
matData <- pred_glm1_1se %>%
mutate(predClass = TotalTreeCover_binom_pred_rounded,
obsClass = TotalTreeCover_binom) %>%
mutate(predClass = replace(predClass, predClass == 0, "no trees"),
predClass = replace(predClass, predClass == 1, "trees"),
obsClass = replace(obsClass, obsClass == 0, "no trees"),
obsClass = replace(obsClass, obsClass == 1, "trees")) %>%
mutate(predClass = as.factor(predClass) ,
obsClass = as.factor(obsClass))
# make matrix as a data.frame
# confMat <- confusionMatrix(data = matData$predClass,
# reference = matData$obsClass)
ConfusionTableR::binary_visualiseR(
train_labels = as.factor(matData$predClass),
truth_labels = as.factor(matData$obsClass),
class_label1 = "No trees",
class_label2 = "Trees",
quadrant_col1 = "wheat",
quadrant_col2 = "darkgreen",
text_col = "white"
)
if ( name_secondBestMod == "Intercept_Only") {
print("The next best lambda model only contains one predictor (an intercept)")
# plot misclassifications against Year
yearResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = jitter(Year), y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = Year, y = missClass)) +
xlab("Year") +
ylab("misclassifications") +
ggtitle("from best lamba model")
# plot misclassifications against Lat
latResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = y, y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = y, y = missClass)) +
xlab("Latitude") +
ylab("misclassifications") +
ggtitle("from best lamba model")
# plot misclassifications against Long
longResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = x, y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = x, y = missClass)) +
xlab("Longitude") +
ylab("misclassifications") +
ggtitle("from best lamba model")
library(patchwork)
(yearResidMod_bestLambda ) /
( latResidMod_bestLambda ) /
( longResidMod_bestLambda )
} else {
# plot misclassifications against Year
yearResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = jitter(Year), y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = Year, y = missClass)) +
xlab("Year") +
ylab("misclassifications") +
ggtitle("from best lamba model")
yearResidMod_1seLambda <- ggplot(pred_glm1_1se) +
geom_point(aes(x = jitter(Year), y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = Year, y = missClass)) +
xlab("Year") +
ylab("misclassifications") +
ggtitle(paste0("from ", name_secondBestMod))
# plot misclassifications against Lat
latResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = y, y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = y, y = missClass)) +
xlab("Latitude") +
ylab("misclassifications") +
ggtitle("from best lamba model")
latResidMod_1seLambda <- ggplot(pred_glm1_1se) +
geom_point(aes(x = y, y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = y, y = missClass)) +
xlab("Latitude") +
ylab("misclassifications") +
ggtitle(paste0("from ", name_secondBestMod))
# plot misclassifications against Long
longResidMod_bestLambda <- ggplot(pred_glm1) +
geom_point(aes(x = x, y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = x, y = missClass)) +
xlab("Longitude") +
ylab("misclassifications") +
ggtitle("from best lamba model")
longResidMod_1seLambda <- ggplot(pred_glm1_1se) +
geom_point(aes(x = x, y = jitter(missClass)), alpha = .1) +
geom_smooth(aes(x = x, y = missClass)) +
xlab("Longitude") +
ylab("misclassifications") +
ggtitle(paste0("from ", name_secondBestMod))
library(patchwork)
(yearResidMod_bestLambda + yearResidMod_1seLambda) /
( latResidMod_bestLambda + latResidMod_1seLambda) /
( longResidMod_bestLambda + longResidMod_1seLambda)
}
Binning predictor variables into “Quantiles”and looking at the mean predicted probability for each percentile.
response_vars <- c("TotalTreeCover_binom", "TotalTreeCover_binom_pred")
# get deciles for best lambda model
if (length(prednames_fig) == 0) {
print("The best lambda model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
pred_glm1_deciles <- predvars2deciles(pred_glm1,
response_vars = response_vars,
pred_vars = prednames_fig,
cut_points = seq(0, 1, 0.01))
}
# get deciles for 1 SE lambda model
if ( name_secondBestMod == "Intercept_Only") {
print("The next best lambda model only contains one predictor (an intercept)")} else {
pred_glm1_deciles_1se <- predvars2deciles(pred_glm1_1se,
response_vars = response_vars,
pred_vars = prednames_secondBestMod,
cut_points = seq(0, 1, 0.01))
}
Below are quantile plots for the best lambda model (note that the predictor variables are scaled)
if (length(prednames_fig) == 0) {
print("The model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
# publication quality version
g3 <- decile_dotplot_pq(df = pred_glm1_deciles, response = c("TotalTreeCover_binom", paste0("TotalTreeCover_binom", "_pred")), IQR = FALSE,
CI = FALSE
) + ggtitle("Decile Plot")
g4 <- add_dotplot_inset(g3, df = pred_glm1_deciles, response = c("TotalTreeCover_binom", paste0("TotalTreeCover_binom", "_pred")), dfRaw = pred_glm1, add_smooth = TRUE, deciles = FALSE)
if(save_figs) {
# png(paste0("../../../Figures/CoverDatFigures/ figures/quantile_plots/quantile_plot_", response, "_",ecoregion,".png"),
# units = "in", res = 600, width = 5.5, height = 3.5 )
# print(g4)
# dev.off()
}
g4
}
Below are percentile plots from the second best lambda model ()
if ( name_secondBestMod == "Intercept_Only") {
print("The next best lambda model only contains one predictor (an intercept)")
} else {
# publication quality version
g3 <- decile_dotplot_pq(pred_glm1_deciles_1se, response = c("TotalTreeCover_binom", paste0("TotalTreeCover_binom", "_pred")), IQR = FALSE) + ggtitle("Decile Plot")
g4 <- add_dotplot_inset(g3, df = pred_glm1_deciles_1se, response = c("TotalTreeCover_binom", paste0("TotalTreeCover_binom", "_pred")), dfRaw = pred_glm1_1se, add_smooth = TRUE, deciles = FALSE)
if(save_figs) {
# png(paste0("figures/quantile_plots/quantile_plot_", response, "_",ecoregion,".png"),
# units = "in", res = 600, width = 5.5, height = 3.5 )
# print(g4)
# dev.off()
}
g4
}
Filtered quantile plots of data. These plots show each vegetation variable, but only based on data that falls into the upper and lower 20th percentiles of each predictor variable.
if (length(prednames_fig) == 0) {
print("The model only contains one predictor (an intercept), so decile plots aren't possible to generate")
} else {
pred_glm1_deciles_filt <- predvars2deciles( pred_glm1,
response_vars = c("TotalTreeCover_binom", paste0("TotalTreeCover_binom", "_pred")),
pred_vars = prednames_fig,
filter_var = TRUE,
filter_vars = prednames_fig,
cut_points = seq(0, 1, 0.01))
g <- decile_dotplot_filtered_pq(df = pred_glm1_deciles_filt, response = "TotalTreeCover_binom",
xvars = prednames_fig)
if(save_figs) {
# jpeg(paste0("figures/quantile_plots/quantile_plot_filtered_mid_v1", , ".jpeg"),
# units = "in", res = 600, width = 5.5, height = 6 )
# g
# dev.off()
}
g
}
Filtered quantile figure with middle 2 deciles also shown
if ( name_secondBestMod == "Intercept_Only") {
print("The next best lambda model only contains one predictor (an intercept)")
} else {
pred_glm1_deciles_filt_mid <- predvars2deciles(pred_glm1,
response_vars = c("TotalTreeCover_binom", paste0("TotalTreeCover_binom", "_pred")),
pred_vars = prednames_fig,
filter_vars = prednames_fig,
filter_var = TRUE,
add_mid = TRUE,
cut_points = seq(0, 1, 0.01))
g <- decile_dotplot_filtered_pq(df = pred_glm1_deciles_filt_mid, response = "TotalTreeCover_binom",
xvars = prednames_fig)
if(save_figs) {
# jpeg(paste0("figures/quantile_plots/quantile_plot_filtered_mid_v1", , ".jpeg"),
# units = "in", res = 600, width = 5.5, height = 6)
# g
# dev.off()
}
g
}